| Description | : Lists the existing projects. |
| Reference | : Link ¶ |
⚶ Changes
{
"#id": "TextAnalysisAuthoring_ListProjects",
"$responses": {
"200": {
"$properties": {
"value": {
"$properties": [
{
"#name": "projectKind",
"Enum": {
"new": [
[
"CustomSingleLabelClassification",
"For building a classification model to classify text using your own data. Each file will have only one label. For example, file 1 is classified as A and file 2 is classified as B."
],
[
"CustomMultiLabelClassification",
"For building a classification model to classify text using your own data. Each file can have one or many labels. For example, file 1 is classified as A, B, and C and file 2 is classified as B and C."
],
[
"CustomEntityRecognition",
"For building an extraction model to identify your domain categories using your own data."
],
[
"CustomAbstractiveSummarization",
"For building an abstractive summarization models which are able to summarize long documents."
],
[
"CustomHealthcare",
"For building an text analytics for health model to identify your health domain data."
],
[
"CustomTextSentiment",
"For building a sentiment models which are able to extract sentiment for long documents."
]
],
"old": [
[
"CustomSingleLabelClassification",
"For building a classification model to classify text using your own data. Each\nfile will have only one label. For example, file 1 is classified as A and file\n2 is classified as B."
],
[
"CustomMultiLabelClassification",
"For building a classification model to classify text using your own data. Each\nfile can have one or many labels. For example, file 1 is classified as A, B,\nand C and file 2 is classified as B and C."
],
[
"CustomEntityRecognition",
"For building an extraction model to identify your domain categories using your\nown data."
],
[
"CustomAbstractiveSummarization",
"For building an abstractive summarization models which are able to summarize\nlong documents."
],
[
"CustomHealthcare",
"For building an text analytics for health model to identify your health domain\ndata."
],
[
"CustomTextSentiment",
"For building a sentiment models which are able to extract sentiment for long\ndocuments."
]
]
}
},
{
"settings": [
{
"#name": "confidenceThreshold",
"Description": {
"new": "The threshold of the class with the highest confidence, at which the prediction will automatically be changed to \"None\". The value of the threshold should be between 0 and 1 inclusive.",
"old": "The threshold of the class with the highest confidence, at which the prediction\nwill automatically be changed to \"None\". The value of the threshold should be\nbetween 0 and 1 inclusive."
}
}
]
},
{
"#name": "language",
"Description": {
"new": "The project language. This is BCP-47 representation of a language. For example, use \"en\" for English, \"en-gb\" for English (UK), \"es\" for Spanish etc.",
"old": "The project language. This is BCP-47 representation of a language. For example,\nuse \"en\" for English, \"en-gb\" for English (UK), \"es\" for Spanish etc."
}
}
]
}
}
},
"default": {
"$properties": {
"error": [
{
"#name": "innererror",
"Description": {
"new": "An object containing more specific information than the current object about the error.",
"old": "An object containing more specific information than the current object about\nthe error."
}
}
]
}
}
}
}
⚼ Request
GET:
/authoring/analyze-text/projects
{
api-version:
string
,
top:
integer
,
skip:
integer
,
maxpagesize:
integer
,
}
⚐ Response (200)
{
value:
[
]
,
{
,
createdDateTime:
string
,
lastModifiedDateTime:
string
,
lastTrainedDateTime:
string
,
lastDeployedDateTime:
string
,
projectKind:
enum
,
storageInputContainerName:
string
,
settings:
{
,
confidenceThreshold:
number
,
amlProjectPath:
string
,
isLabelingLocked:
boolean
,
runGptPredictions:
boolean
,
gptPredictiveLookahead:
integer
,
}
projectName:
string
,
multilingual:
boolean
,
description:
string
,
language:
string
,
}
nextLink:
string
,
}
⚐ Response (default)
{
error:
}
{
,
code:
enum
,
message:
string
,
target:
string
,
details:
[
,
string
,
]
innererror:
}
{
,
code:
enum
,
message:
string
,
details:
object
,
target:
string
,
innererror:
string
,
}